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Agent Architecture Considerations for Real-Time Planning in Games

Agent Architecture Considerations for Real-Time Planning in Games. Jeff Orkin Monolith Productions. F.E.A.R. Agenda. Motivation Problems Solutions Was it worth it?. Agenda. Motivation – Why plan? Problems – Performance! Solutions – Agent architecture Was it worth it?.

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Agent Architecture Considerations for Real-Time Planning in Games

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  1. Agent Architecture Considerations for Real-Time Planning in Games Jeff Orkin Monolith Productions

  2. F.E.A.R.

  3. Agenda • Motivation • Problems • Solutions • Was it worth it?

  4. Agenda • Motivation – Why plan? • Problems – Performance! • Solutions – Agent architecture • Was it worth it?

  5. Why plan in real-time?

  6. Goal-Oriented Behavior

  7. Goal-Oriented Behavior Problems: • Managing dependencies • Sharing behaviors

  8. Problem: Dependencies

  9. Problem: Dependencies

  10. Problem: Dependencies

  11. Problem: Dependencies

  12. Problem: Dependencies

  13. No One Lives Forever 2 TRON 2.0 Problem: Sharing

  14. No One Lives Forever 2 TRON 2.0 Problem: Sharing

  15. No One Lives Forever 2 TRON 2.0 Problem: Sharing

  16. P.D.D.L. • Planning Domain Definition Language • Goals • Desired state • Actions • Preconditions • Effects

  17. P.D.D.L. Goal: (define (problem get-paid) (:domain briefcase-world) (:init (place home) (place office) (object p) (object d) (object b) (at B home) (at P home) (at D home) (in P)) (:goal (and (at B office) (at D office) (at P home))))

  18. P.D.D.L. Action: (:action put-in :parameters (?x - physob ?l - location) :precondition (not (= ?x B)) :effect (when (and (at ?x ?l) (at B ?l)) (in ?x)) ) • Other actions: take-out, move

  19. P.D.D.L. • Modular • Goals • Actions • Decoupled Modules Related by symbols • World State • Preconditions • Effects • Applied PDDL’s structure to C++ toolkit in game code.

  20. Monolith Productions Management

  21. How to Plan in Real-Time and Keep Your Job Jeff Orkin Monolith Productions

  22. The Plan Goal: KillEnemy Plan: Goto (couch) UseObject (couch) Goto (coverNode) AttackFromCover

  23. AI Performance Guideline: 1.0ms / frame

  24. AI Performance: Off the Chart!

  25. Preconditions: Visibility AI Performance: Off the Chart!

  26. Preconditions: Visibility Pathfinding AI Performance: Off the Chart!

  27. Preconditions: Visibility Pathfinding Tactical Position Validity AI Performance: Off the Chart!

  28. Solution Re-consider Agent Architecture • Distributed processing • Caching

  29. Solution Re-consider Agent Architecture • Distributed processing • Caching Inspiration: • MIT Media Lab’s C4 • Robotics

  30. Solution

  31. What is a soldier?

  32. What is a soldier?

  33. What is a soldier?

  34. Sensors: See What is a soldier?

  35. Sensors: See Hear What is a soldier?

  36. Sensors: See Hear Feel pain What is a soldier?

  37. Subsystems: Navigate / Move What is a soldier?

  38. Subsystems: Navigate / Move Attention Selection (Targeting) What is a soldier?

  39. Subsystems: Navigate / Move Attention Selection (Targeting) Weapons What is a soldier?

  40. Distributed Processing with Sensors • Pseudo-parallel • Amortize precondition processing across many frames • Allow incremental processing

  41. Distributed Processing with Sensors • More than Vision, Hearing, Touch • Tactical analysis • Internal desires

  42. Distributed Processing with Sensors • Update: • Every frame • Periodic Polling • Event-driven • Limit total number of expensive sensor updates per frame.

  43. Sensor Example: CoverNode Sensor • Update 3 times / second

  44. Sensor Example: Cover Node Sensor • Update 3 times / second

  45. Sensor Example: PassTarget Sensor • Incremental update

  46. Sensor Example: PassTarget Sensor • Incremental update

  47. Sensor Example: PassTarget Sensor • Incremental update

  48. Sensor Example: PassTarget Sensor • Incremental update

  49. Sensor Example: PassTarget Sensor • Incremental update

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